Spot the difference with SiMBolS

Throughout data-driven sciences one is confronted with large amounts of data, which might appear very similar on a first glance. Especially in the realm of molecular dynamics simulations, one readily generates thousands of simulation snapshots, each of which containing the location of thousands of atoms.

Over the years, methods have been developed to often compare a simulation trajectory to single frames or single frames among each other. Sometimes, however, two whole simulation trajectories need to be compared to each other, which makes the choice of measure more ambiguous. To make all choices feasible and assist in the decision, we have crafted SiMBols (SImilarity Measures for biOLogical Systems) that supplies the tools and the framework in an easy-to-use python package. Check out the latest version of SiMBols at our git repository here.

Recent Publications

Across atoms to crossing continents: Application of similarity measures to biological location data, Fabian Schuhmann, Leonie Ryvkin, James D. McLaren, Luca Gerhards, Ilia A. Solov'yov, PLoS ONE, 18, e0284736, (2023)
Computational Reconstruction and Analysis of Structural Models of Avian Cryptochrome 4, Maja Hanić, Fabian Schuhmann, Anders Frederiksen, Corinna Langebrake, Georg Manthey, Miriam Liedvogel, Jingjing Xu, Henrik Mouritsen, Ilia A. Solov'yov, Journal of Physical Chemistry B, 126, 4623-4635, (2022)
The same, but different, but still the same: structural and dynamical differences of neutrophil elastase and cathepsin G, Fabian Schuhmann, Xiangyin Tan, Luca Gerhards, Heloisa N. Bordallo, Ilia A. Solov'yov, European Physical Journal D, 76, 126-(1-14), (2022)
Exploring Post-activation Conformational Changes in Pigeon Cryptochrome 4, Fabian Schuhmann, Daniel R. Kattnig, Ilia A. Solov'yov, Journal of Physical Chemistry B, 125, 9652-9659, (2021)